Bias and Fairness in AI-Based Medical Tools: A Critical Examination
Keywords:
Artificial Intelligence, bias in AI, fairness, healthcare disparities, machine learning, algorithmic fairness, medical ethics, predictive analytics, healthcare outcomes, regulation in AI.Abstract
The increasing reliance on Artificial Intelligence (AI) in healthcare raises important ethical concerns, particularly regarding bias and fairness in AI-based medical tools. This critical examination explores how AI systems, including machine learning algorithms and neural networks, can perpetuate or even exacerbate healthcare disparities when applied to medical diagnostics, treatment recommendations, and predictive analytics. While AI holds the potential to improve the efficiency and accuracy of medical decision-making, its widespread use also risks reinforcing historical biases embedded in training data. These biases can arise from factors such as race, gender, socioeconomic status, and geography, which are often reflected in the datasets used to train AI models.
